Efficacy of Artificial Intelligence for Gatekeeping in Referrals to Specialized Care

Summary

In Rio Grande do Sul, Brazil, the demand for specialty care referrals has increased sharply with the adoption of the electronic regulatory system, especially in rural areas. In 2023 alone, over 79,000 referrals were submitted monthly, totaling 1.7 million annual gatekeeping decisions. Due to workforce limitations, nearly 70% of referrals are authorized automatically, often without clinical validation. This leads to delays for high-risk patients, unnecessary specialist visits, and a growing backlog, currently over 172,000 pending referrals. To address this, an AI algorithm was developed to triage referrals based on urgency and appropriateness.

The investigators propose a prospective controlled study with randomized implementation of the AI tool across selected specialty queues in the electronic referral system. The population will consist of referrals from specialties waitlists from municipalities in Rio Grande do Sul. Specialties to be included will be selected by the State Health Department prospectively according to gatekeeping needs. The intervention will be an AI-based triage algorithm. The control will be a standard gatekeeping process. The primary outcome is the proportion of referrals with a final decision (authorized or redirected to primary care) within six months; secondary outcomes include time to decision and appointment, system-level performance metrics. Referrals will be randomly assigned to algorithmic or human gatekeeping with a 1:1 ratio. The algorithm classifies referrals into two groups: not authorized (pending more data or teleconsultation), authorized. Authorization cases are further divided into routine and high-risk referrals to help the manage demand. Each AI prediction provides a probability from 0 to 1 of authorization (or deferring). The implementation threshold is set at 0.8; cases below this level will be classified as low confidence for decision and will not be included. According to the State Health Department's decisions, several referral lines are expected to be selected for the intervention. A sample size 934 (467 per arm) for each included specialty was calculated to detect a 1.2 relative risk for the primary outcome with 90% power and 5% significance.

Conditions

• Primary Care
• Primary Care Patients With Chronic Conditions

Interventions

OTHER

Standard gatekeeping

Human evaluators (mostly physicians) review referrals and determine, based on established protocols, whether they should be authorized.

OTHER

AI algorithm

An AI algorithm was developed to perform the first evaluation (triaging) of the referrals inserted in the electronic referral system from the Rio Grande do Sul Health Department.

OTHER

Subsequent interactions between primary care and regulation system

After the first evaluation of a referral, several subsequent rounds of interaction between gatekeepers and primary care physicians can be conducted to further detail patient needs and urgency.

Sponsors & Collaborators

• Rio Grande do Sul State Health Department - SES/RS

  collaborator OTHER_GOV

• Hospital de Clinicas de Porto Alegre

  lead OTHER

Principal Investigators

• Dimitris V. Rados, Ph.D. · TelessaúdeRS

Study Design

Allocation

RANDOMIZED

Purpose

HEALTH_SERVICES_RESEARCH

Masking

NONE

Model

PARALLEL

Eligibility

Sex

ALL

Healthy Volunteers

No

Timeline & Regulatory

Start

2025-11-15

Primary Completion

2028-12-31

Completion

2029-12-31

Countries

• Brazil

Study Locations